Paper Citation Record · LEDGER

Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.06040.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.06040 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:05:36.163556Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T20:30:08.074175Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d1450774-821f-4966-8f22-07e060dd05ca · inbound

Vector Boson Fusion Signatures of Superheavy Majorana Neutrinos at Muon Colliders cites this paper.

Vector Boson Fusion Signatures of Superheavy Majorana Neutrinos at Muon Colliders Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:36.163556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd1ad33-8e48-4656-bb9b-c8ba37055af5 · inbound

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits cites this paper.

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:04:37.094861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5292a658-7237-4ce3-bd6f-60c3d6f2aedc · inbound

Complementary probes of Bilinear RPV SUSY models with a wino-like LSP via Neutrino Oscillation and LHC cites this paper.

Complementary probes of Bilinear RPV SUSY models with a wino-like LSP via Neutrino Oscillation and LHC Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:30:08.075521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c68ca16c-487e-44af-97e5-967728480049 · inbound

Multiboson Signatures of Doubly Charged Scalars at a Same-Sign Muon Collider cites this paper.

Multiboson Signatures of Doubly Charged Scalars at a Same-Sign Muon Collider Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Reference 110

Resolution: unresolved; no resolver link, observed 2026-08-01T23:40:25.115880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7831607-613c-4b74-9265-93a7c02a186d · inbound

Benchmarking Machine Learning Architectures for ttH Multilepton Signal Sensitivity cites this paper.

Benchmarking Machine Learning Architectures for ttH Multilepton Signal Sensitivity Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Reference 1

Resolution: malformed identifier; no resolver link, observed 2026-08-01T16:24:30.473929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.